mlsanityData to 7 Oct 2026

A Globally Normalized Neural Model for Semantic Parsing

arXiv 2106.03376 ↗First posted 7 Jun 2021cs.CLcited by 2

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Abstract (arXiv)

In this paper, we propose a globally normalized model for context-free grammar (CFG)-based semantic parsing. Instead of predicting a probability, our model predicts a real-valued score at each step and does not suffer from the label bias problem. Experiments show that our approach outperforms locally normalized models on small datasets, but it does not yield improvement on a large dataset.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 33 references that are in the map. First posted on arXiv 7 Jun 2021.

Builds on

20 of 33 references.

References · 20

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8 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 1.

2 papers.

  1. 2022An Empirical Study on the Overlapping Problem of Open-Domain Dialogue Datasetscited by 8
  2. 2023Semantic-aware Contrastive Learning for More Accurate Semantic Parsingcited by 2